{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Polygon at 0x20473dd6e48>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "plt.plot([1,2,6],lw=1)\n",
    "plt.axvspan(1.0, 1.2, facecolor='g', alpha=0.3, **dict())#垂直x轴区域\n",
    "plt.axhspan(4.0, 5.2, facecolor='pink', alpha=0.3, **dict())#垂直y轴区域"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}